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Out-of-Distribution (OoD) detection has developed substantially in the pastfew years, with available methods approaching, and in a few cases achieving,perfect data separation on standard benchmarks. These results generally involvelarge or complex models, pretraining, exposure to OoD examples or extrahyperparameter tuning. Remarkably, it is possible to achieve results that canexceed many of these state-of-the-art methods with a very simple method. Wedemonstrate that ResNet18 trained with Supervised Contrastive Learning (SCL)produces state-of-the-art results out-of-the-box on near and far OoD detectionbenchmarks using only Euclidean distance as a scoring rule. This may obviatethe need in some cases for more sophisticated methods or larger models, and atthe very least provides a very strong, easy to use baseline for furtherexperimentation and analysis.

SupEuclid: Extremely Simple, High Quality OoD Detection with Supervised Contrastive Learning and Euclidean Distance